Collection of microsatellite genetic data from multiple projects involving southern mule deer (Odocoileus hemionus fuliginatus) in San Diego County, California. Samples were collected 2005-2007, 2010, 2012-2013, 2015, and 2018-2020.

The goal of this project was to primarily assess east-west connectivity across Route 67 and secondarily, north-south connectivity across Scripps Poway Parkway and Poway Road, two highly trafficked roads to the west of Route 67. We collected deer scat piles from both sides of these crossings in spring between March and June of 2015, and again in fall throughout October 2015. Collected pellets were extracted and amplified using protocols and loci developed by Bohonak and Mitelberg (2014). DNA fingerprinting results were used to investigate mule deer movement in the region directly by identifying individuals which had been captured multiple times throughout the study period. We also assessed movement indirectly through pedigree reconstruction and population structure analyses. The shapefile below contains 126 deer scat piles and the genotypes obtained at 15 microsatellite markers and one sex specific genetic marker. These represented 71 unique individual genotypes (45 females, 26 males) with 9 recapture events and 46 resampling events (defined as scat piles collected at the same location within one week of each other and matching the same unique genotype). These data support the following publicaton; Mitelberg, A. & Vandergast, A.G. (2016) Non-invasive genetic sampling of Southern Mule Deer ( Odocoileus hemionus fuliginatus) reveals limited movement across California State Route 67 in San Diego County. Western Wildlife, 3, 8-18. References: Bohonak, A., Mitelberg, A. 2014. Social Structure and Genetic Connectivity in the Southern Mule Deer: Implications for Management. San Diego State University. San Diego: Report Prepared for California Department of Fish and Game.

We developed and report a microsatellite data set composed of 52 microsatellite loci for 2305 individuals from 20 bison conservation herds (17 US federal, 1 tribal, 2 Canadian) and a single nucleotide polymorphism (SNP) data set composed of 5013 biallic loci for 376 individuals from 16 bison conservation herds that were used as part of a broader study. We also developed an algorithm to select a subset of SNPs that captures the genetic variation present in the full SNP data set. Human expansion is a major driver of both declining wildlife species abundance and the contraction of species’ distributions, increasing the risk of genetic erosion and the need for genetic monitoring. Rapidly advancing technology has expanded the types of genetic data that are available for wildlife conservation. However, the use of different genetic markers could result in different management decisions and, thus, must be considered carefully. Rebounding from near extinction in the early 1900s, the majority of plains bison (Bison bison bison) are managed as small and isolated herds. Microsatellite-based analyses are currently used to inform management of the US federal bison conservation herds. Transitioning from monitoring with tens of multiallelic loci (e.g., microsatellite loci) to thousands of biallelic loci (e.g., SNP loci) could increase genotyping efficiency and improve the precision of population genetic inference but would require an understanding of the inferential differences between genetic marker types.

Dataset containing information for white-tailed deer samples from Ohio, Pennsylvania, Maryland, Virginia and New York, genotyped for 11 microsatellites markers. Marker OvirQ should not be used as it presents alleles inconsistent with reported pattern, with some alleles separated by only 1 base pair and inconsistent between runs. Projected coordinates representing sampling location are in a user-defined CRS, similar to USA Contiguous Albers Equal Area Conic: "+proj=aea +lat_1=29.3 +lat_2=45.3 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs".

Dataset containing information for white-tailed deer samples from Ohio, Pennsylvania, Maryland, Virginia and New York, genotyped for 11 microsatellites markers. Marker OvirQ should not be used as it presents alleles inconsistent with reported pattern, with some alleles separated by only 1 base pair and inconsistent between runs. Projected coordinates representing sampling location are in a user-defined CRS, similar to USA Contiguous Albers Equal Area Conic: "+proj=aea +lat_1=29.3 +lat_2=45.3 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs".

This data set describes nuclear microsatellite genotypes derived from 17 autosomal loci (Sug001, Sug002, Sug003, Sug006, Sug007, Sug008, Sug009, Sug010, Sug011, Sug012, Sug019, Sug021, Sug022, Sug023, Sug024, Sug029, Sug038) in shrews, Sorex spp.

This data set describes nuclear microsatellite genotypes derived from 17 autosomal loci (Sug001, Sug002, Sug003, Sug006, Sug007, Sug008, Sug009, Sug010, Sug011, Sug012, Sug019, Sug021, Sug022, Sug023, Sug024, Sug029, Sug038) in shrews, Sorex spp.

We developed and report a microsatellite data set composed of 52 microsatellite loci for 2305 individuals from 20 bison conservation herds (17 US federal, 1 tribal, 2 Canadian) and a single nucleotide polymorphism (SNP) data set composed of 5013 biallic loci for 376 individuals from 16 bison conservation herds that were used as part of a broader study. We also developed an algorithm to select a subset of SNPs that captures the genetic variation present in the full SNP data set. Human expansion is a major driver of both declining wildlife species abundance and the contraction of species’ distributions, increasing the risk of genetic erosion and the need for genetic monitoring. Rapidly advancing technology has expanded the types of genetic data that are available for wildlife conservation. However, the use of different genetic markers could result in different management decisions and, thus, must be considered carefully. Rebounding from near extinction in the early 1900s, the majority of plains bison (Bison bison bison) are managed as small and isolated herds. Microsatellite-based analyses are currently used to inform management of the US federal bison conservation herds. Transitioning from monitoring with tens of multiallelic loci (e.g., microsatellite loci) to thousands of biallelic loci (e.g., SNP loci) could increase genotyping efficiency and improve the precision of population genetic inference but would require an understanding of the inferential differences between genetic marker types.

This data set contains sampling information, allele sizes of 11 microsatellite loci, and Genbank accession numbers of ddRAD seq results for scoters (Melanitta sp.). Microsatellite data is provided for three North American species of scoter (black scoter, M. americana n = 61; white-winged scoter, M. deglandi, n = 208; surf scoter, M. perspicillata, n = 145) and their European congeners (common scoter, M. nigra, n = 19; velvet scoter, M. fusca, n = 20). Individuals with ddRAD results include 27 black scoters, 4 common scoters, 32 surf scoters, 28 white-winged scoters, and 4 velvet scoters.

This data set contains sampling information, allele sizes of 11 microsatellite loci, and Genbank accession numbers of ddRAD seq results for scoters (Melanitta sp.). Microsatellite data is provided for three North American species of scoter (black scoter, M. americana n = 61; white-winged scoter, M. deglandi, n = 208 ; surf scoter, M. perspicillata, n = 145) and their European congeners (common scoter, M. nigra, n = 19; velvet scoter, M. fusca, n = 20 ). Individuals with ddRAD results include 27 black scoters, 4 common scoters, 32 surf scoters, 28 white-winged scoters, and 4 velvet scoters.